贷款
随机森林
计算机科学
逻辑回归
信用风险
违约概率
消费(社会学)
数据挖掘
精算学
业务
财务
机器学习
社会科学
社会学
标识
DOI:10.1109/icmss56787.2023.10117838
摘要
With the continuous development of economy and the improvement of people's level, personal credit loan develops rapidly. Because the problem of credit loan default is becoming more and more serious, the development of credit loan business needs an accurate prediction system. Banks have a lot of historical data, using data mining technology, from the basic information, social relations, consumption behavior, such as address information as much as possible in the massive amounts of customer data mining on the information of the borrowers, summed up the main factors influencing the personal credit risk prediction, and based on this model can effectively predict the personal credit risk related. The results show that the prediction accuracy of XGBoost model is higher than that of Logistic Regression model and Random Forest model.
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